Additive Pattern Database Heuristics

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3.2 Computing the Heuristic Value of a State●●partition the n tiles into n/2 non-overlappingpairs, then sum the pairwise distances for eachof the chosen pairsTo get the most accurate admissible heuristic,we want a partition that maximizes the sum ofthe pairwaise distances
●For each state of the search , this maximizingpartition may be different, requiring the partitioningto be performed for each heuristic evaluation.Thus, the authors use the term dynamicallypartitionedaddititive pattern database heuristics●To compute the heuristic for a given state, usemutual-cost graph
Page 1 and 2: Additive Pattern Database Heuristic
Page 3 and 4: ●●The most effective way to imp
Page 5 and 6: ●●●An optimal solution to the
Page 7 and 8: 1.3 two main contributions of thisp
Page 9 and 10: 2. Previous Work on AdmissibleHeuri
Page 11 and 12: Statically-Partitioned Additivedata
Page 13: 3. Dynamically-Partitioned Database
Page 17 and 18: Dynamically-Partitioned databaseHeu
Page 19 and 20: 4.2.2 fifteen puzzle partition
Page 21 and 22: 4.2.3 Twenty-four puzzle
Page 23 and 24: 4.2.4 Thirty-five puzzle
Page 25 and 26: 4.2.5 Discussion of the tile puzzle
Page 27 and 28: Experimental results for the 15-dis
Page 29 and 30: 6. Vertex Cover
Page 32 and 33: 7. General Characterization of theM
Page 34 and 35: 7.2.2 Partial pattern databases●
Page 36 and 37: The steps for the staticallypartiti
Page 38 and 39: 7.4 Differences between theapplicat
Page 40 and 41: 8.2 Conclusions and Further Work●

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